{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6f31e6e8-d43b-4619-8372-91c97120c9a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模拟订单数据条数： 1200\n",
      "\n",
      "校验通过：汇总结果与 Excel 模板完全一致！\n",
      "     月份  月度销量  季度销量\n",
      "0    1月  2354  7780\n",
      "1    2月  1902  7780\n",
      "2    3月  3524  7780\n",
      "3    4月  2698  8157\n",
      "4    5月  2896  8157\n",
      "5    6月  2563  8157\n",
      "6    7月  3156  9673\n",
      "7    8月  2896  9673\n",
      "8    9月  3621  9673\n",
      "9   10月  2635  8387\n",
      "10  11月  2963  8387\n",
      "11  12月  2789  8387\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "\n",
    "# ===================== 0. Matplotlib 中文 & 随机种子 =====================\n",
    "# 这里用黑体显示中文，如果你的电脑没有 SimHei，可以改成 'Microsoft YaHei'\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "np.random.seed(42)\n",
    "random.seed(42)\n",
    "Faker.seed(42)\n",
    "fake = Faker('zh_CN')\n",
    "\n",
    "\n",
    "# ===================== 1. 目标月度销量（来自 Excel 模板） =====================\n",
    "TARGET_MONTHLY_SALES = {\n",
    "    1: 2354,\n",
    "    2: 1902,\n",
    "    3: 3524,\n",
    "    4: 2698,\n",
    "    5: 2896,\n",
    "    6: 2563,\n",
    "    7: 3156,\n",
    "    8: 2896,\n",
    "    9: 3621,\n",
    "    10: 2635,\n",
    "    11: 2963,\n",
    "    12: 2789,\n",
    "}\n",
    "\n",
    "\n",
    "def split_integer(total: int, n_parts: int) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    把 total 拆成 n_parts 份正整数，和仍然为 total。\n",
    "    用 Dirichlet 分配 + 调整余数，保证每一份 >= 1。\n",
    "    \"\"\"\n",
    "    if total < n_parts:\n",
    "        # 理论上不会出现这个情况，这里做个保护\n",
    "        n_parts = total\n",
    "\n",
    "    base = np.ones(n_parts, dtype=int)\n",
    "    remaining = total - n_parts\n",
    "\n",
    "    if remaining == 0:\n",
    "        return base\n",
    "\n",
    "    weights = np.random.dirichlet(alpha=np.ones(n_parts))\n",
    "    extra = np.floor(weights * remaining).astype(int)\n",
    "    diff = remaining - extra.sum()\n",
    "    # 把剩余的 diff 均匀地加到前 diff 个元素上\n",
    "    for i in range(diff):\n",
    "        extra[i] += 1\n",
    "\n",
    "    return base + extra\n",
    "\n",
    "\n",
    "# ===================== 2. 生成模拟 erp_order 订单数据 =====================\n",
    "def generate_erp_orders(orders_per_month: int = 100) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    基于给定 Schema，生成不少于 1000 条模拟订单数据。\n",
    "    按照 TARGET_MONTHLY_SALES 控制每个月 quantity 汇总值，\n",
    "    确保聚合后的“月度销量”与 Excel 模板完全一致。\n",
    "    \"\"\"\n",
    "    records = []\n",
    "    order_id = 1\n",
    "\n",
    "    for month in range(1, 13):\n",
    "        year = 2021\n",
    "        total_qty = TARGET_MONTHLY_SALES[month]\n",
    "\n",
    "        # 每个月生成固定数量的订单\n",
    "        n_orders = orders_per_month\n",
    "        if total_qty < n_orders:\n",
    "            n_orders = total_qty\n",
    "\n",
    "        # 拆分数量，使得该月所有订单 quantity 之和 = 目标值\n",
    "        quantities = split_integer(total_qty, n_orders)\n",
    "\n",
    "        # 该月起止时间\n",
    "        start_dt = datetime(year, month, 1, 0, 0, 0)\n",
    "        if month == 12:\n",
    "            end_dt = datetime(year, 12, 31, 23, 59, 59)\n",
    "        else:\n",
    "            end_dt = datetime(year, month + 1, 1, 0, 0, 0) - timedelta(seconds=1)\n",
    "\n",
    "        for q in quantities:\n",
    "            order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "            payment_date = order_time + timedelta(minutes=random.randint(5, 720))\n",
    "            shipping_date = payment_date + timedelta(days=random.randint(0, 7))\n",
    "\n",
    "            unit_price = round(random.uniform(80, 400), 2)\n",
    "            product_amount = round(unit_price * q, 2)\n",
    "            original_price = round(unit_price + random.uniform(10, 80), 2)\n",
    "\n",
    "            record = {\n",
    "                \"id\": order_id,\n",
    "                \"internal_order_number\": fake.uuid4(),\n",
    "                \"online_order_number\": fake.uuid4(),\n",
    "                \"store_name\": random.choice([\"旗舰店\", \"直营店\", \"线上店\"]),\n",
    "                \"full_channel_user_id\": fake.uuid4()[:16],\n",
    "                \"shipping_date\": shipping_date,\n",
    "                \"payment_date\": payment_date,\n",
    "                \"payable_amount\": product_amount,\n",
    "                \"paid_amount\": product_amount,\n",
    "                \"status\": \"已完成\",\n",
    "                \"consignee\": fake.name(),\n",
    "                \"spu\": f\"SPU{random.randint(1, 50):04d}\",\n",
    "                \"order_time\": order_time,\n",
    "                \"province\": fake.province(),\n",
    "                \"city\": fake.city_name(),\n",
    "                \"platform\": random.choice([\"天猫\", \"京东\", \"抖音\"]),\n",
    "                \"sub_order_number\": fake.uuid4(),\n",
    "                \"online_sub_order_number\": fake.uuid4(),\n",
    "                \"original_online_order_number\": fake.uuid4(),\n",
    "                \"sku\": f\"SKU{random.randint(1, 500):06d}\",\n",
    "                \"quantity\": int(q),\n",
    "                \"unit_price\": unit_price,\n",
    "                \"product_name\": random.choice([\"保湿水\", \"洁面乳\", \"精华液\", \"面霜\"]),\n",
    "                \"color_and_spec\": random.choice([\"50ml\", \"100ml\", \"套装\"]),\n",
    "                \"product_amount\": product_amount,\n",
    "                \"original_price\": original_price,\n",
    "                \"is_gift\": random.choice([\"否\", \"是\"]),\n",
    "                \"sub_order_status\": \"正常\",\n",
    "                \"refund_status\": random.choice([\"未申请退款\", \"成功退款\", \"退款关闭\"]),\n",
    "                \"registered_quantity\": None,\n",
    "                \"actual_refund_quantity\": 0,\n",
    "            }\n",
    "\n",
    "            records.append(record)\n",
    "            order_id += 1\n",
    "\n",
    "    df = pd.DataFrame(records)\n",
    "    return df\n",
    "\n",
    "\n",
    "# ===================== 3. 生成“月份-月度销量-季度销量”汇总表 =====================\n",
    "def build_summary_table(df_erp: pd.DataFrame) -> pd.DataFrame:\n",
    "    df_erp[\"month\"] = df_erp[\"order_time\"].dt.month\n",
    "    monthly_qty = (\n",
    "        df_erp.groupby(\"month\")[\"quantity\"].sum().reindex(range(1, 13))\n",
    "    )\n",
    "\n",
    "    # 计算季度总销量\n",
    "    q1 = monthly_qty.loc[1:3].sum()\n",
    "    q2 = monthly_qty.loc[4:6].sum()\n",
    "    q3 = monthly_qty.loc[7:9].sum()\n",
    "    q4 = monthly_qty.loc[10:12].sum()\n",
    "\n",
    "    quarter_map = {\n",
    "        1: q1, 2: q1, 3: q1,\n",
    "        4: q2, 5: q2, 6: q2,\n",
    "        7: q3, 8: q3, 9: q3,\n",
    "        10: q4, 11: q4, 12: q4,\n",
    "    }\n",
    "\n",
    "    summary = pd.DataFrame({\n",
    "        \"月份\": [f\"{m}月\" for m in range(1, 13)],\n",
    "        \"月度销量\": monthly_qty.values,\n",
    "        \"季度销量\": [quarter_map[m] for m in range(1, 13)],\n",
    "    })\n",
    "    return summary\n",
    "\n",
    "\n",
    "def build_target_summary_table() -> pd.DataFrame:\n",
    "    # 按你截图里的数据手动写死目标表\n",
    "    months = [f\"{m}月\" for m in range(1, 13)]\n",
    "    monthly = [\n",
    "        2354, 1902, 3524,\n",
    "        2698, 2896, 2563,\n",
    "        3156, 2896, 3621,\n",
    "        2635, 2963, 2789,\n",
    "    ]\n",
    "    q1, q2, q3, q4 = 7780, 8157, 9673, 8387\n",
    "    quarterly = [q1, q1, q1, q2, q2, q2, q3, q3, q3, q4, q4, q4]\n",
    "\n",
    "    return pd.DataFrame({\n",
    "        \"月份\": months,\n",
    "        \"月度销量\": monthly,\n",
    "        \"季度销量\": quarterly,\n",
    "    })\n",
    "\n",
    "\n",
    "# ===================== 4. 画图：复刻深色季度分块柱状图 =====================\n",
    "def plot_sales(summary: pd.DataFrame):\n",
    "    # 画布和背景\n",
    "    fig, ax = plt.subplots(figsize=(14, 8))\n",
    "    bg_color = \"#050B2A\"  # 深蓝背景\n",
    "    fig.patch.set_facecolor(bg_color)\n",
    "    ax.set_facecolor(bg_color)\n",
    "\n",
    "    # 调整上下边距，给标题和注释留空间\n",
    "    plt.subplots_adjust(top=0.78, bottom=0.18, left=0.06, right=0.97)\n",
    "\n",
    "    # X 轴\n",
    "    x = np.arange(1, 13)\n",
    "    monthly = summary[\"月度销量\"].values\n",
    "\n",
    "    # 按季度着色\n",
    "    quarter_bg_colors = [\"#152452\", \"#3B2726\", \"#38312D\", \"#271A3F\"]\n",
    "    bar_colors = [\n",
    "        \"#3B57F8\", \"#2B46D0\", \"#546CFF\",      # Q1\n",
    "        \"#FF7A1E\", \"#FF8B3A\", \"#FF963D\",      # Q2\n",
    "        \"#FFB320\", \"#FFA21A\", \"#FFC13C\",      # Q3\n",
    "        \"#E255A2\", \"#F06BB8\", \"#D2438F\"       # Q4\n",
    "    ]\n",
    "\n",
    "    # 先画季度背景块（axvspan）\n",
    "    quarter_spans = [(0.5, 3.5), (3.5, 6.5), (6.5, 9.5), (9.5, 12.5)]\n",
    "    for (x0, x1), color in zip(quarter_spans, quarter_bg_colors):\n",
    "        ax.axvspan(x0, x1, facecolor=color, alpha=0.8, zorder=0)\n",
    "\n",
    "    # 再画柱子\n",
    "    bar_width = 0.55\n",
    "    ax.bar(x, monthly, width=bar_width, color=bar_colors, zorder=2)\n",
    "\n",
    "    # 柱顶数据标签\n",
    "    for xv, yv in zip(x, monthly):\n",
    "        ax.text(\n",
    "            xv, yv + 80, str(yv),\n",
    "            ha=\"center\", va=\"bottom\", color=\"white\",\n",
    "            fontsize=12, zorder=3\n",
    "        )\n",
    "\n",
    "    # 季度总销量数字\n",
    "    q_values = [\n",
    "        summary.loc[0:2, \"季度销量\"].iloc[0],    # Q1\n",
    "        summary.loc[3:5, \"季度销量\"].iloc[0],    # Q2\n",
    "        summary.loc[6:8, \"季度销量\"].iloc[0],    # Q3\n",
    "        summary.loc[9:11, \"季度销量\"].iloc[0],   # Q4\n",
    "    ]\n",
    "    quarter_x = [2, 5, 8, 11]\n",
    "    quarter_y = monthly.max() + 500\n",
    "    for xv, qv in zip(quarter_x, q_values):\n",
    "        ax.text(\n",
    "            xv, quarter_y, str(qv),\n",
    "            ha=\"center\", va=\"bottom\", color=\"white\",\n",
    "            fontsize=14, zorder=3\n",
    "        )\n",
    "\n",
    "    # X 轴样式\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(summary[\"月份\"].tolist(), fontsize=14, color=\"white\")\n",
    "\n",
    "    # Y 轴隐藏（只看柱子和标签）\n",
    "    ax.set_yticks([])\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    # 主标题 & 副标题\n",
    "    fig.text(\n",
    "        0.5, 0.93,\n",
    "        \"2021年各月化妆品销量走势\",\n",
    "        ha=\"center\", va=\"center\",\n",
    "        fontsize=30, color=\"white\", weight=\"bold\"\n",
    "    )\n",
    "    fig.text(\n",
    "        0.5, 0.88,\n",
    "        \"2021年第三季度销量最多9673，9月单月销量最大3621\",\n",
    "        ha=\"center\", va=\"center\",\n",
    "        fontsize=18, color=\"white\"\n",
    "    )\n",
    "\n",
    "    # 注释\n",
    "    fig.text(\n",
    "        0.05, 0.06,\n",
    "        \"*注：数据来源于公司销售系统，统计日期截至2021.12.31\",\n",
    "        ha=\"left\", va=\"center\",\n",
    "        fontsize=12, color=\"white\"\n",
    "    )\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# ===================== 5. 主程序 =====================\n",
    "if __name__ == \"__main__\":\n",
    "    # 1) 生成模拟订单数据\n",
    "    df_erp = generate_erp_orders(orders_per_month=100)\n",
    "    print(\"模拟订单数据条数：\", len(df_erp))\n",
    "\n",
    "    # 2) 生成汇总表并与目标表进行严格校验\n",
    "    summary = build_summary_table(df_erp)\n",
    "    summary_target = build_target_summary_table()\n",
    "\n",
    "    # 如果不一致会抛 AssertionError\n",
    "    pd.testing.assert_frame_equal(\n",
    "        summary.reset_index(drop=True),\n",
    "        summary_target.reset_index(drop=True)\n",
    "    )\n",
    "    print(\"\\n校验通过：汇总结果与 Excel 模板完全一致！\")\n",
    "    print(summary)\n",
    "\n",
    "    # 3) 按模板风格画图\n",
    "    plot_sales(summary)\n"
   ]
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